issue: #34357
Go Parquet uses dictionary encoding by default, and it will fall back to
plain encoding if the dictionary size exceeds the dictionary size page
limit. Users can specify custom fallback encoding by using
`parquet.WithEncoding(ENCODING_METHOD)` in writer properties. However,
Go Parquet [fallbacks to plain
encoding](e65c1e295d/go/parquet/file/column_writer_types.gen.go.tmpl (L238))
rather than custom encoding method users provide. Therefore, this patch
only turns off dictionary encoding for the primary key.
With a 5 million auto ID primary key benchmark, the parquet file size
improves from 13.93 MB to 8.36 MB when dictionary encoding is turned
off, reducing primary key storage space by 40%.
Signed-off-by: shaoting-huang <shaoting.huang@zilliz.com>
issue: #34123
Benchmark case: The benchmark run the go benchmark function
`BenchmarkDeltalogFormat` which is put in the Files changed. It tests
the performance of serializing and deserializing from two different data
formats under a 10 million delete log dataset.
Metrics: The benchmarks measure the average time taken per operation
(ns/op), memory allocated per operation (MB/op), and the number of
memory allocations per operation (allocs/op).
| Test Name | Avg Time (ns/op) | Time Comparison | Memory Allocation
(MB/op) | Memory Comparison | Allocation Count (allocs/op) | Allocation
Comparison |
|---------------------------------|------------------|-----------------|---------------------------|-------------------|------------------------------|------------------------|
| one_string_format_reader | 2,781,990,000 | Baseline | 2,422 | Baseline
| 20,336,539 | Baseline |
| pk_ts_separate_format_reader | 480,682,639 | -82.72% | 1,765 | -27.14%
| 20,396,958 | +0.30% |
| one_string_format_writer | 5,483,436,041 | Baseline | 13,900 |
Baseline | 70,057,473 | Baseline |
| pk_and_ts_separate_format_writer| 798,591,584 | -85.43% | 2,178 |
-84.34% | 30,270,488 | -56.78% |
Both read and write operations show significant improvements in both
speed and memory allocation.
Signed-off-by: shaoting-huang <shaoting.huang@zilliz.com>
add sparse float vector support to different milvus components,
including proxy, data node to receive and write sparse float vectors to
binlog, query node to handle search requests, index node to build index
for sparse float column, etc.
https://github.com/milvus-io/milvus/issues/29419
---------
Signed-off-by: Buqian Zheng <zhengbuqian@gmail.com>
shaoting-huang
smellthemoon
Cai Yudong
Buqian Zheng
yah01
congqixia
jaime
Enwei Jiao
Xiaofan
SimFG
xige-16
godchen
XuanYang-cn
shaoyue
dragondriver
Xiangyu Wang
sunby
neza2017
quicksilver